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PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | A substantial body of evidence suggested that this chemical compound might act as an endocrine-disrupting agent, with documented associations with diverse health complications including metabolic disorders, oncological conditions, and digestive pathologies . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Notably, epidemiological studies had identified correlations between prenatal MEP exposure and cognitive impairment in offspring , while cross-sectional research in the United States had implicated MEP as a potential risk factor for cognitive decline in the geriatric population . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | However, the precise molecular mechanisms through which MEP might directly induce or exacerbate AD pathogenesis remain inadequately validated. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Our investigation sought to address this knowledge gap by exploring the potential association between MEP exposure and AD development, with particular emphasis on elucidating the underlying biological mechanisms. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The following was the specific analysis process (Fig. 1). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 1Flowchart for studying the effects of Methylparaben on Alzheimer’s disease (Schematic of the study workflow: MEP 3D structure acquisition/optimization → multi-database target screening and intersection analysis (153 common targets) → PPI network construction and core target identification (10 genes) → GO/KEGG enrichment analysis → machine learning-based diagnostic validation (10 algorithms) and SHAP interpretation → molecular docking → in vitro qRT-PCR verification with SK-N-SH cells (three groups) to elucidate the molecular mechanisms of MEP-induced AD-related tau pathology) Flowchart for studying the effects of Methylparaben on Alzheimer’s disease (Schematic of the study workflow: MEP 3D structure acquisition/optimization → multi-database target screening and intersection analysis (153 common targets) → PPI network construction and core target identification (10 genes) → GO/KEGG enrichment analysis → machine learning-based diagnostic validation (10 algorithms) and SHAP interpretation → molecular docking → in vitro qRT-PCR verification with SK-N-SH cells (three groups) to elucidate the molecular mechanisms of MEP-induced AD-related tau pathology) To systematically evaluate the toxicological properties of MEP, we employed two recognized tools with established reliability in toxicological prediction. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | First, Swiss-ADME (a free web tool for in silico ADME property prediction, https://swissadme.ch/), which was used to assess the impact of MEP on the blood-brain barrier (BBB). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Second, Protox 3.0 (a platform for in silico toxicity prediction, https://tox.charite.de/protox3/index.php?site=home), which was applied to classify the toxicity category of MEP. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | These tools were selected for their ability to integrate multidimensional chemical and biological data, thereby ensuring the scientific validity of MEP toxicity predictions. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | For details on the predictive methodology of these tools, please refer to Reference . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The initial three-dimensional (3D) structure of MEP was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/, CID:7456). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Structural optimization was performed using Chem3D software (version 14.0.0.17). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The MM2 force field was applied for energy minimization until the root mean square (RMS) gradient reached 0.01 kcal/(mol·Å), to obtain a stable and low-energy 3D conformation. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The optimized structure was used for subsequent analyses (e.g., molecular docking). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Subsequently, targets were obtained using SMILES in the SwissTargetPrediction and SEA databases. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | By removing all duplicate targets, we ensure that only potential MEP targets unique to the ‘Homo sapiens’ species were retained . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The keyword ‘Alzheimer’s disease’ was used to identify targets related to AD in the OMIM and GeneCards databases . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | An intersection analysis was then performed using a Venn diagram generated with R software to obtain the shared targets between AD and MEP . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | We used STRING (https://cn.string-db.org/, version 12.0) to analyze the common targets between MEP and AD, with the species restricted to Homo sapiens. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | A high-confidence interaction threshold (minimum score > 0.9) was set based on three core rationales: (1) alignment with the “Highest confidence” level of the STRING scoring system, which integrates multi-dimensional evidence (e.g., gene co-expression, literature mining) to minimize false-positive interactions; (2) the need to filter non-specific associations for precise identification of AD-related core targets, avoiding network redundancy; and (3) consistency with standard protocols in neurodegenerative disease-related network toxicology studies . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | After constructing the PPI network (145 nodes, 596 edges), the data were imported into Cytoscape (version 3.7.0) for topological analysis . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Core regulatory genes were identified through a standardized workflow. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | First, calculating the degree centrality of all nodes using the Network Analyzer module (undirected network, weight threshold consistent with STRING score > 0.9). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Second, initial screening of candidate nodes with degree values in the top 20%. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Third, secondary sorting via the CytoHubba module’s Maximum Cluster Centrality (MCC) algorithm (default parameters: clustering coefficient threshold = 0.1, shortest path calculation = Dijkstra’s algorithm), and finally, integrating the two ranking results to determine the core targets . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | To elucidate the functional profiles of target genes, we performed functional enrichment analyses using the clusterProfiler package in R, which enables simultaneous GO annotation and KEGG pathway analysis through integrated biological database mining . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | For statistical significance, we set the threshold of P < 0.05 (adjusted P-value, Padj < 0.05) to filter enriched GO terms and KEGG pathways, ensuring only biologically meaningful functional items were retained for subsequent analysis. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | To screen diagnostic markers for MEP-related AD, we built a multi-algorithm machine learning framework with rigorous data processing and parameter optimization. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Gene expression data came from GEO’s GSE5281 (74 normal, 87 AD samples) and GSE138260 (19 normal, 17 AD samples), undergoing preprocessing: k-NN imputation (k = 5) for < 5% missing values (excluding > 5%), RMA normalization to eliminate batch effects, z-score scaling (mean = 0, SD = 1) for 10 core targets, and stratified partitioning into 70% training (n = 138) and 30% validation (n = 59) sets. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Leveraging gene expression profile data from the validation cohort, ten classic machine learning algorithms (including Partial Least Squares (PLS), Random Forest (RF), Decision Tree Stacking (DTS), Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Neural Network, and glmBoost) were employed to develop predictive models. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Overfitting was avoided via 5-fold cross-validation (training set stability), regularization (L2 for Logistic Regression/SVM, pruning for XGBoost/GBM), and early stopping (10 epochs for Neural Network, 5 rounds for XGBoost). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Primary evaluation used AUC (> 0.7 for good performance), with accuracy, precision, recall, and F1-score as secondary metrics to assess core genes’ diagnostic value . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | We used the Shapley additive explanations (SHAP) algorithm to quantify the contribution of each feature to the predictive outcomes. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | This method assigns a SHAP value to each feature, making it possible to assess its influence on the model’s predictions in an interpretable way. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The optimized 3D structure of MEP was uploaded into the CB-Dock2 platform, charged with H atoms, and then subjected to free energy minimization . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | In addition, the protein crystal structures obtained from the PDB database, were processed by adding hydrogen atoms and removing all water molecules. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The binding affinity energy (kcal/mol) was computed using CB-Dock2, which projected the optimal docking model with the least amount of energy. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Human neuroblastoma cell line SK-N-SH was obtained from Puri Biotech (Wuhan, China, Item No. CL-0214). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Cells were cultured in DMEM-F12 (Thermo Fisher Scientific, Massachusetts, USA, Item No. 11320033) supplemented with 10% heat-inactivated fetal bovine serum (TransSerum FQ Fetal Bovine Serum, Beijing, China, Item No. C0232) and 1% penicillin-streptomycin (New Cell & Molecular Biotech, Jiangsu, China, Item No. C125C5) . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | To clarify the regulatory effect of methylparaben (Selleck, Shanghai, China, Item No. S3985, Purity: 99.95%) on the transcriptional expression of core genes related to AD-related tau pathology model, we detected the mRNA expression levels of the ten core genes using quantitative reverse transcription polymerase chain reaction (qRT-PCR). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Three groups of parallel samples were set up in the experiment: the normal control group (NC group, SK-N-SH neural cells without any pathological intervention), the AD model group (AD group, an AD-related tau pathology cell model constructed by intervention with 20 nmol/L okadaic acid (MedChemExpress, America, Item No. HY-N6785, Purity: ≥ 99.19%) for 24 h), and the MEP intervention group (SK-N-SH cells that were first treated with 20 nmol/L okadaic acid to establish an AD model and then treated with 3 µg/L MEP for another 24 h) . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Subsequently, total RNA was extracted after 24 h of treatment with MEP. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Reverse transcription was performed using SuperScript™III First-Strand Synthesis SuperMix (Thermo Fisher, America, Item No. 11752-050) . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to analyze the mRNA expression of ten genes (Table 1). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | qRT-PCR was conducted using Power SYBR Green Master Mix (Applied Biosystems, America, Item No. 4367659) . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Amplification conditions were as follows: 95 ◦C for 1 min, followed by 40 cycles of 95 ◦C for 15 s, and 63 ◦C for 25 s. Dissociation curve analysis was performed to confirm primer specificity, and β-actin served as an internal control. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The expression level of the target RNA was calculated based on the threshold cycle (Ct) value using the comparative Ct method, where the relative expression level R = 2 . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Here, ΔCt = Cttarget gene−Ctreference gene (with β-actin serving as the reference gene), and ΔΔCt = ΔCtexperimental group − ΔCt control group . |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Table 1qRT-PCR primers and conditionsGeneGenbank AccessionPrimer Sequences(5’to3’)Size(bp)Annealing (℃)β-actinNM_001101.3GATGACCCAGATCATGTTTGAGAC11260GGAGTCCATCACGATGCCAGTHIF1AAF208487GCAGTTCCGCAAGCCCTGAAAG11160CAGTGGTGGCAGTGGTAGTGGTIGF1RNM_000875.5CCTGCACAACTCCATCTTCGTG12560CGGTGATGTTGTAGGTGTCTGCPDGFRBNM_002609.4TGCAGACATCGAGTCCTCCAAC10960GCTTAGCACTGGAGACTCGTTGPTK2NM_005607.4GACCTGAGCGAGTTCATCAAG19860CTTGGTGCTGATGTCCTTGGTVCAM1NM_001078.4GCTGTCCTGATGCTGTTTCTG18760CAGGTGATGTTGCTGATGTTGACXCL12NM_002096.6TTGCTGCTTTAGCTGCCTTC21260CAGATCCGCAGCTCTGAAACERBB2NM_004448.4AGCCAACGTGTTCAGTGAAA19560TGGTCATAGGGCACGTAGGTESR1NM_000125.4GCTTACTGACCAACCTGGCAGA12960GGATCTCTAGCCAGGCACATTCJAK2NM_004972.4CAGAGCCTACAGCAAGATGG20360GGTCTTGGTGATGTCCTTGABCL2L1NM_001191.3GAGCTGGTGGTTGACTTTCT17860CAGGTATGCACCCAGAGTGA qRT-PCR primers and conditions All results of this study were analyzed using the GraphPad Prism 8 software (version 8.01) for t-tests on two groups and one-way ANOVA on three groups. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | P < 0.05 or lower is considered statistically significant. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | All data were presented as the mean ± standard deviation (SD). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Swiss-ADME analysis indicated that MEP has a significant impact on the blood-brain barrier (BBB) (Supplementary Table S1 and Fig. 2A), while Protox 3.0 classified MEP as a Category IV toxic substance (Fig. 2B). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | These findings were consistent with previous literature reports on MEP-mediated human toxicity, and lay the foundation for our further in-depth investigations into the toxic effects of MEP on the human body in a systematic and profound manner. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 2The results of toxicity analysis (A) Swiss-ADME analysis showing MEP’s ability to affect blood-brain barrier (BBB) integrity; (B) Protox 3.0 prediction: MEP LD50 = 2000 mg/kg, toxicity Category IV, average similarity = 100%, consistent with known toxicological data The results of toxicity analysis (A) Swiss-ADME analysis showing MEP’s ability to affect blood-brain barrier (BBB) integrity; (B) Protox 3.0 prediction: MEP LD50 = 2000 mg/kg, toxicity Category IV, average similarity = 100%, consistent with known toxicological data A total of 209 MEP targets from the SwissTargetPrediction and SEA databases were subjected to screening. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Furthermore, a total of 15,976 Alzheimer’s disease targets were identified from the GeneCards and OMIM databases. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Following the elimination of duplicate targets, a total of 153 overlapping targets were obtained (Fig. 3). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 3Venn diagrams of intersection genes (Venn diagram (R software) showing 209 MEP targets (SwissTargetPrediction/SEA) and 15,976 AD targets (GeneCards/OMIM), with 153 overlapping targets as core research objects) Venn diagrams of intersection genes (Venn diagram (R software) showing 209 MEP targets (SwissTargetPrediction/SEA) and 15,976 AD targets (GeneCards/OMIM), with 153 overlapping targets as core research objects) We constructed a protein-protein interaction (PPI) network using the STRING database, yielding 145 nodes and 596 edges. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Simultaneously, we performed an in-depth analysis of the topological properties of network nodes using Cytoscape software (Fig. 4A). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Based on degree values, we ultimately identified HIF1A, ESR1, ERBB2, CXCL12, VCAM1, PDGFRB, JAK2, PTK2, IGF1R, and BCL2L1 as core targets influencing MEP in AD (Fig. 4B). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 4PPI network of targets associated with AD (A) STRING-derived PPI network (Homo sapiens, confidence > 0.9): 145 nodes, 596 edges (node size = interaction strength, edge thickness = confidence); (B) Top 10 core targets (Cytoscape: Degree + MCC algorithm): HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2, BCL2L1 (darker color = higher core degree) PPI network of targets associated with AD (A) STRING-derived PPI network (Homo sapiens, confidence > 0.9): 145 nodes, 596 edges (node size = interaction strength, edge thickness = confidence); (B) Top 10 core targets (Cytoscape: Degree + MCC algorithm): HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2, BCL2L1 (darker color = higher core degree) GO and KEGG enrichment analyses were performed to functionally interpret the shared targets (P < 0.05, Padj < 0.05). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | A total of 1508 enriched GO biological functions were identified, including 1263 biological processes (e.g., positive regulation of kinase activity, regulation of membrane potential), 78 cellular components (e.g., synaptic membrane, postsynaptic membrane), and 167 molecular functions (e.g., neurotransmitter receptor activity, hydro-lyase activity). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Additionally, 28 significantly enriched KEGG pathways were detected, with key pathways including neuroactive ligand-receptor interaction (Padj = 9.0 × 10) and GABAergic synapse (Padj = 0.002). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | These findings suggest that candidate genes play a critical role in AD (Fig. 5). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 5Enrichment analysis of potential targets (A) GO enrichment (P < 0.05, Padj < 0.05): 1263 biological processes, 78 cellular components, 167 molecular functions (e.g., kinase activity regulation, synaptic membrane localization); (B) KEGG enrichment: 28 pathways, including neuroactive ligand-receptor interaction (Padj = 9.0 × 10⁻⁵) and GABAergic synapse (Padj = 0.002) Enrichment analysis of potential targets (A) GO enrichment (P < 0.05, Padj < 0.05): 1263 biological processes, 78 cellular components, 167 molecular functions (e.g., kinase activity regulation, synaptic membrane localization); (B) KEGG enrichment: 28 pathways, including neuroactive ligand-receptor interaction (Padj = 9.0 × 10⁻⁵) and GABAergic synapse (Padj = 0.002) To further validate the clinical relevance and predictive value of the core genes, this study first conducted empirical analysis using a clinical dataset (merged GSE5281 and GSE138260). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Results showed (Fig. 6) that there were significant differences in the expression levels of the ten core genes (HIF1A, ESR1, ERBB2, CXCL12, VCAM1, PDGFRB, JAK2, PTK2, IGF1R, and BCL2L1) between AD samples and normal tissue samples (P < 0.05, P < 0.01), which initially confirmed the close association of these core genes with the pathological progression of AD. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Subsequently, a multi-algorithm machine learning prediction framework was constructed based on gene expression profile data. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Ten classic machine learning algorithms, including Partial Least Squares (PLS), Random Forest (RF), Decision Tree Stacking (DTS), Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Neural Network, and glmBoost, were employed to validate the diagnostic potential of the aforementioned core genes. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | It is important to clarify that the data used in this predictive model analysis are derived from predictive research, which differs from the clinical sample data in Fig. 6. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Receiver Operating Characteristic (ROC) curve analysis showed that their Area Under the Curve (AUC) values were all greater than 0.70 (Fig. 7), confirming the reliable diagnostic potential of these core genes for AD. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The results indicate that ten core genes exhibit optimal predictive capability under the SVW model (AU = 0.889). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | To clarify the contribution of each core gene to the predictive model, the Shapley Additive Explanations (SHAP) algorithm was used for model interpretability analysis. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Results showed (Fig. 8A) that IGF1R (SHAP value = 0.200) and HIF1A (SHAP value = 0.073) were the two key factors with the greatest impact on the prediction results. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | A violin plot displayed the expression distribution characteristics of the core genes under different conditions (Fig. 8B), where the width represents data density and colors distinguish expression levels. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | A scatter plot revealed the distribution of SHAP values of key genes and their direction of influence on prediction results, with a color gradient intuitively reflecting the regulatory effect of IGF1R and HIF1A expression levels (Fig. 8C). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Force-directed analysis further indicated (Fig. 8D) that IGF1R (8.89, Δ = 0.194) acted as a major positive regulatory factor, while HIF1A (9.77, Δ = −0.198) served as a major negative regulatory factor. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Together, they drove the predicted value (f(x) = 0.313) below the baseline expectation (E[f(x)] = 0.521). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The consistent results from the above clinical validation and predictive model analysis suggest that IGF1R and HIF1A have potential diagnostic value for AD and are expected to become novel biomarkers or therapeutic targets, providing references for subsequent AD-related research and the development of treatment strategies. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 6The expression of core genes in AD (Box plot of 10 core genes’ mRNA levels (GEO datasets GSE5281/GSE138260). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Red boxes = AD samples, blue boxes = normal samples. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Statistical significance: *P < 0.05, **P < 0.01, ***P < 0.001) The expression of core genes in AD (Box plot of 10 core genes’ mRNA levels (GEO datasets GSE5281/GSE138260). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Red boxes = AD samples, blue boxes = normal samples. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Statistical significance: *P < 0.05, **P < 0.01, ***P < 0.001) Fig. 7Comparison of receiver operating characteristic (ROC) curves for different machine (ROC curves of 10 algorithms (PLS, RF, SVM, etc.). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | AUC > 0.7 for all models; SVM performs best (AUC = 0.889, 95% CI: 0.801–0.977). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Abscissa = 1-specificity, ordinate = sensitivity) Comparison of receiver operating characteristic (ROC) curves for different machine (ROC curves of 10 algorithms (PLS, RF, SVM, etc.). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | AUC > 0.7 for all models; SVM performs best (AUC = 0.889, 95% CI: 0.801–0.977). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Abscissa = 1-specificity, ordinate = sensitivity) Fig. 8Identification of core genes in MEP-mediated tau pathogenesis of AD (A) SHAP feature importance: IGF1R (SHAP = 0.200) and HIF1A (SHAP = 0.073) are key; (B) Violin plot: gene expression distribution (width = data density, color = expression level); (C) SHAP scatter plot: gene expression vs. SHAP value (color gradient = regulatory direction); (D) Force-directed analysis: IGF1R (Δ = 0.194, positive regulator) and HIF1A (Δ = −0.198, negative regulator) Identification of core genes in MEP-mediated tau pathogenesis of AD (A) SHAP feature importance: IGF1R (SHAP = 0.200) and HIF1A (SHAP = 0.073) are key; (B) Violin plot: gene expression distribution (width = data density, color = expression level); (C) SHAP scatter plot: gene expression vs. SHAP value (color gradient = regulatory direction); (D) Force-directed analysis: IGF1R (Δ = 0.194, positive regulator) and HIF1A (Δ = −0.198, negative regulator) Molecular docking analyses were conducted to investigate the interactions between MEP and ten core targets: HIF1A (PDB ID: 5L9V), IGF1R (PDB ID: 3LW0), PDGFRB (PDB ID: 1H9O), PTK2 (PDB ID: 6YOJ), VCAM1 (PDB ID: 1IJ9), CXCL12 (PDB ID: 4LMQ), ERBB2 (PDB ID: 8U8X), ESR1 (PDB ID: 8BZC), JAK2 (PDB ID: 8BXH) and BCL2L1 (PDB ID: 7JGW). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The docking results obtained from CB-Dock2 revealed binding energies ranging from − 5.0 to −6.0 kcal/mol between MEP and target proteins (Supplementary Table S2). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Generally, a binding energy of less than 0 indicates binding activity, whereas values below − 5.0 kcal/mol indicate strong binding activity, with lower values denoting stronger interactions. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The results demonstrated strong affinities between MEP and the ten core targets, indicating spontaneous binding and suggesting their critical role in the molecular mechanisms of MEP-mediated tau pathogenesis of AD. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The docking results were visualised in 2D representations using CB-Dock2 (Fig. 9). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 9The optimal docking results of HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2, and BCL2L1 (2D docking schematics (CB-Dock2) of MEP with targets (PDB IDs: 5L9V, 3LW0, etc.). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Dashed lines = intermolecular interactions (hydrogen bonds, hydrophobic forces). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Binding energies: −5.0 to −6.0 kcal/mol) The optimal docking results of HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2, and BCL2L1 (2D docking schematics (CB-Dock2) of MEP with targets (PDB IDs: 5L9V, 3LW0, etc.). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Dashed lines = intermolecular interactions (hydrogen bonds, hydrophobic forces). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Binding energies: −5.0 to −6.0 kcal/mol) The qRT-PCR results (Fig. 10) showed that compared with the AD-related tau pathology model group, the mRNA expression levels of the ten genes in the MEP intervention group were significantly increased (*P < 0.05, **P < 0.001). |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | In contrast, the mRNA expression levels of these genes in the normal control group (NC group) were lower than those in both the MEP-mediated tau pathogenesis of AD group and the MEP intervention group. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | These results were highly consistent with the validation results of the clinical dataset, further confirming the key role of genes in the MEP-mediated tau pathogenesis of AD. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | The core significance of this finding lay in the following aspects: it verified at the cellular and molecular level that MEP could affect the AD-related tau pathological process by upregulating the transcriptional expression of these genes, thereby providing direct experimental evidence for the hypothesis that MEP exposure is associated with the risk of AD. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Meanwhile, these ten genes might serve as core molecular targets linking MEP to AD-related tau pathology, laying a key experimental foundation for further exploring the molecular mechanism of MEP-induced AD-related tau pathology and developing targeted intervention strategies. |
PMC12829224 | Integrating machine learning and experiments to elucidate the potential molecular mechanisms of methylparaben-induced Alzheimer’s disease: evidence from a Tau hyperphosphorylation cell model | Fig. 10qRT-PCR was used to detect the regulatory effects of MEP on the ten core genes (Bar graph of 10 core genes’ relative mRNA levels (2 method, β-actin as internal control). |
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